Hypothesis

Postnatal environmental exposures, particularly those found in household products and dietary intake, along with specific serum metabolomics profiles, are significantly associated with the BMI Z-score of children aged 6-11 years. Higher concentrations of certain metabolites in serum, reflecting exposure to chemical classes or metals, will correlate with variations in BMI Z-score, controlling for age and other relevant covariates. High-dimensional metabolomics data can reveal comprehensive biochemical profiles that reflect environmental exposures and metabolic states. Not only that, but some metabolites associated with chemical exposures and dietary patterns can serve as biomarkers for the risk of developing obesity.

Background

Research indicates that postnatal exposure to endocrine-disrupting chemicals (EDCs) such as phthalates, bisphenol A (BPA), and polychlorinated biphenyls (PCBs) can significantly influence body weight and metabolic health (Junge et al., 2018). These chemicals, commonly found in household products and absorbed through dietary intake, are linked to detrimental effects on body weight and metabolic health in children. This hormonal interference can lead to an increased body mass index (BMI) in children, suggesting a potential pathway through which exposure to these chemicals contributes to the development of obesity.

A longitudinal study on Japanese children examined the impact of postnatal exposure (first two years of life) to p,p’-dichlorodiphenyltrichloroethane (p,p’-DDT) and p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) through breastfeeding (Plouffe et al., 2020). The findings revealed that higher levels of these chemicals in breast milk were associated with increased BMI at 42 months of age. DDT and DDE may interfere with hormonal pathways related to growth and development. These chemicals can mimic or disrupt hormones that regulate metabolism and fat accumulation. This study highlights the importance of understanding how persistent organic pollutants can affect early childhood growth and development.

The study by Harley et al. (2013) investigates the association between prenatal and postnatal Bisphenol A (BPA) exposure and various body composition metrics in children aged 9 years from the CHAMACOS cohort. The study found that higher prenatal BPA exposure was linked to a decrease in BMI and body fat percentages in girls but not boys, suggesting sex-specific effects. Conversely, BPA levels measured at age 9 were positively associated with increased adiposity in both genders, highlighting the different impacts of exposure timing on childhood development.

The 2022 study 2022 study by Uldbjerg et al. explored the effects of combined exposures to multiple EDCs, suggesting that mixtures of these chemicals can have additive or synergistic effects on BMI and obesity risk. Humans are typically exposed to a mixture of chemicals rather than individual EDCs, making it crucial to understand how these mixtures might interact. The research highlighted that the interaction between different EDCs can lead to additive (where the effects simply add up) or even synergistic (where the combined effect is greater than the sum of their separate effects) outcomes. These interactions can significantly amplify the risk factors associated with obesity and metabolic disorders in children. The dose-response relationship found that even low-level exposure to multiple EDCs could result in significant health impacts due to their combined effects.

These studies collectively illustrate the critical role of environmental EDCs in shaping metabolic health outcomes in children, highlighting the necessity for ongoing research and policy intervention to mitigate these risks.

Data Description

This study will utilize data from the subcohort of 1301 mother-child pairs in the HELIX study, who are which aged 6-11 years for whom complete exposure and outcome data were available. Exposure data included detailed dietary records after pregnancy and concentrations of various chemicals like BPA and PCBs in child blood samples. There are categorical and numerical variables, which will include both demographic details and biochemical measurements. This dataset allows for robust statistical analysis to identify potential associations between EDC exposure and changes in BMI Z-scores, considering confounding factors such as age, gender, and socioeconomic status. There are no missing data so there is not need to impute the information. Child BMI Z-scores were calculated based on WHO growth standards.

load(paste0(work.dir, "/HELIX.RData"))
filtered_codebook <- codebook %>%
  filter(domain %in% c("Chemicals", "Lifestyles") & period == "Postnatal" & subfamily != "Allergens")
kable(filtered_codebook, align = "c", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F)
variable_name domain family subfamily period location period_postnatal description var_type transformation labels labelsshort
h_bfdur_Ter h_bfdur_Ter Lifestyles Lifestyle Diet Postnatal NA NA Breastfeeding duration (weeks) factor Tertiles Breastfeeding Breastfeeding
hs_bakery_prod_Ter hs_bakery_prod_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: bakery products (hs_cookies + hs_pastries) factor Tertiles Bakery prod BakeProd
hs_beverages_Ter hs_beverages_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: beverages (hs_dietsoda+hs_soda) factor Tertiles Soda Soda
hs_break_cer_Ter hs_break_cer_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: breakfast cereal (hs_sugarcer+hs_othcer) factor Tertiles BF cereals BFcereals
hs_caff_drink_Ter hs_caff_drink_Ter Lifestyles Lifestyle Diet Postnatal NA NA Drinks a caffeinated or æenergy drink (eg coca-cola, diet-coke, redbull) factor Tertiles Caffeine Caffeine
hs_dairy_Ter hs_dairy_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: dairy (hs_cheese + hs_milk + hs_yogurt+ hs_probiotic+ hs_desert) factor Tertiles Dairy Dairy
hs_fastfood_Ter hs_fastfood_Ter Lifestyles Lifestyle Diet Postnatal NA NA Visits a fast food restaurant/take away factor Tertiles Fastfood Fastfood
hs_KIDMED_None hs_KIDMED_None Lifestyles Lifestyle Diet Postnatal NA NA Sum of KIDMED indices, without index9 numeric None KIDMED KIDMED
hs_mvpa_prd_alt_None hs_mvpa_prd_alt_None Lifestyles Lifestyle Physical activity Postnatal NA NA Clean & Over-reporting of Moderate-to-Vigorous Physical Activity (min/day) numeric None PA PA
hs_org_food_Ter hs_org_food_Ter Lifestyles Lifestyle Diet Postnatal NA NA Eats organic food factor Tertiles Organicfood Organicfood
hs_proc_meat_Ter hs_proc_meat_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: processed meat (hs_coldmeat+hs_ham) factor Tertiles Processed meat ProcMeat
hs_readymade_Ter hs_readymade_Ter Lifestyles Lifestyle Diet Postnatal NA NA Eats a æready-made supermarket meal factor Tertiles Ready made food ReadyFood
hs_sd_wk_None hs_sd_wk_None Lifestyles Lifestyle Physical activity Postnatal NA NA sedentary behaviour (min/day) numeric None Sedentary Sedentary
hs_total_bread_Ter hs_total_bread_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: bread (hs_darkbread+hs_whbread) factor Tertiles Bread Bread
hs_total_cereal_Ter hs_total_cereal_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: cereal (hs_darkbread + hs_whbread + hs_rice_pasta + hs_sugarcer + hs_othcer + hs_rusks) factor Tertiles Cereals Cereals
hs_total_fish_Ter hs_total_fish_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: fish and seafood (hs_canfish+hs_oilyfish+hs_whfish+hs_seafood) factor Tertiles Fish Fish
hs_total_fruits_Ter hs_total_fruits_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: fruits (hs_canfruit+hs_dryfruit+hs_freshjuice+hs_fruits) factor Tertiles Fruits Fruits
hs_total_lipids_Ter hs_total_lipids_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: Added fat factor Tertiles Diet fat Diet fat
hs_total_meat_Ter hs_total_meat_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: meat (hs_coldmeat+hs_ham+hs_poultry+hs_redmeat) factor Tertiles Meat Meat
hs_total_potatoes_Ter hs_total_potatoes_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: potatoes (hs_frenchfries+hs_potatoes) factor Tertiles Potatoes Potatoes
hs_total_sweets_Ter hs_total_sweets_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: sweets (hs_choco + hs_sweets + hs_sugar) factor Tertiles Sweets Sweets
hs_total_veg_Ter hs_total_veg_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: vegetables (hs_cookveg+hs_rawveg) factor Tertiles Vegetables Vegetables
hs_total_yog_Ter hs_total_yog_Ter Lifestyles Lifestyle Diet Postnatal NA NA Food group: yogurt (hs_yogurt+hs_probiotic) factor Tertiles Yogurt Yogurt
hs_dif_hours_total_None hs_dif_hours_total_None Lifestyles Lifestyle Sleep Postnatal NA NA Total hours of sleep (mean weekdays and night) numeric None Sleep Sleep
hs_as_c_Log2 hs_as_c_Log2 Chemicals Metals As Postnatal NA NA Arsenic (As) in child numeric Logarithm base 2 As As
hs_cd_c_Log2 hs_cd_c_Log2 Chemicals Metals Cd Postnatal NA NA Cadmium (Cd) in child numeric Logarithm base 2 Cd Cd
hs_co_c_Log2 hs_co_c_Log2 Chemicals Metals Co Postnatal NA NA Cobalt (Co) in child numeric Logarithm base 2 Co Co
hs_cs_c_Log2 hs_cs_c_Log2 Chemicals Metals Cs Postnatal NA NA Caesium (Cs) in child numeric Logarithm base 2 Cs Cs
hs_cu_c_Log2 hs_cu_c_Log2 Chemicals Metals Cu Postnatal NA NA Copper (Cu) in child numeric Logarithm base 2 Cu Cu
hs_hg_c_Log2 hs_hg_c_Log2 Chemicals Metals Hg Postnatal NA NA Mercury (Hg) in child numeric Logarithm base 2 Hg Hg
hs_mn_c_Log2 hs_mn_c_Log2 Chemicals Metals Mn Postnatal NA NA Manganese (Mn) in child numeric Logarithm base 2 Mn Mn
hs_mo_c_Log2 hs_mo_c_Log2 Chemicals Metals Mo Postnatal NA NA Molybdenum (Mo) in child numeric Logarithm base 2 Mo Mo
hs_pb_c_Log2 hs_pb_c_Log2 Chemicals Metals Pb Postnatal NA NA Lead (Pb) in child numeric Logarithm base 2 Pb Pb
hs_tl_cdich_None hs_tl_cdich_None Chemicals Metals Tl Postnatal NA NA Dichotomous variable of thallium (Tl) in child factor None Tl Tl
hs_dde_cadj_Log2 hs_dde_cadj_Log2 Chemicals Organochlorines DDE Postnatal NA NA Dichlorodiphenyldichloroethylene (DDE) in child adjusted for lipids numeric Logarithm base 2 DDE DDE
hs_ddt_cadj_Log2 hs_ddt_cadj_Log2 Chemicals Organochlorines DDT Postnatal NA NA Dichlorodiphenyltrichloroethane (DDT) in child adjusted for lipids numeric Logarithm base 2 DDT DDT
hs_hcb_cadj_Log2 hs_hcb_cadj_Log2 Chemicals Organochlorines HCB Postnatal NA NA Hexachlorobenzene (HCB) in child adjusted for lipids numeric Logarithm base 2 HCB HCB
hs_pcb118_cadj_Log2 hs_pcb118_cadj_Log2 Chemicals Organochlorines PCBs Postnatal NA NA Polychlorinated biphenyl -118 (PCB-118) in child adjusted for lipids numeric Logarithm base 2 PCB 118 PCB118
hs_pcb138_cadj_Log2 hs_pcb138_cadj_Log2 Chemicals Organochlorines PCBs Postnatal NA NA Polychlorinated biphenyl-138 (PCB-138) in child adjusted for lipids numeric Logarithm base 2 PCB 138 PCB138
hs_pcb153_cadj_Log2 hs_pcb153_cadj_Log2 Chemicals Organochlorines PCBs Postnatal NA NA Polychlorinated biphenyl-153 (PCB-153) in child adjusted for lipids numeric Logarithm base 2 PCB 153 PCB153
hs_pcb170_cadj_Log2 hs_pcb170_cadj_Log2 Chemicals Organochlorines PCBs Postnatal NA NA Polychlorinated biphenyl-170 (PCB-170) in child adjusted for lipids numeric Logarithm base 2 PCB 170 PCB170
hs_pcb180_cadj_Log2 hs_pcb180_cadj_Log2 Chemicals Organochlorines PCBs Postnatal NA NA Polychlorinated biphenyl-180 (PCB-180) in child adjusted for lipids numeric Logarithm base 2 PCB 180 PCB180
hs_sumPCBs5_cadj_Log2 hs_sumPCBs5_cadj_Log2 Chemicals Organochlorines PCBs Postnatal NA NA Sum of PCBs in child adjusted for lipids (4 cohorts) numeric Logarithm base 2 PCBs SumPCB
hs_dep_cadj_Log2 hs_dep_cadj_Log2 Chemicals Organophosphate pesticides DEP Postnatal NA NA Diethyl phosphate (DEP) in child adjusted for creatinine numeric Logarithm base 2 DEP DEP
hs_detp_cadj_Log2 hs_detp_cadj_Log2 Chemicals Organophosphate pesticides DETP Postnatal NA NA Diethyl thiophosphate (DETP) in child adjusted for creatinine numeric Logarithm base 2 DETP DETP
hs_dmdtp_cdich_None hs_dmdtp_cdich_None Chemicals Organophosphate pesticides DMDTP Postnatal NA NA Dichotomous variable of dimethyl dithiophosphate (DMDTP) in child factor None DMDTP DMDTP
hs_dmp_cadj_Log2 hs_dmp_cadj_Log2 Chemicals Organophosphate pesticides DMP Postnatal NA NA Dimethyl phosphate (DMP) in child adjusted for creatinine numeric Logarithm base 2 DMP DMP
hs_dmtp_cadj_Log2 hs_dmtp_cadj_Log2 Chemicals Organophosphate pesticides DMTP Postnatal NA NA Dimethyl thiophosphate (DMTP) in child adjusted for creatinine numeric Logarithm base 2 DMDTP DMTP
hs_pbde153_cadj_Log2 hs_pbde153_cadj_Log2 Chemicals Polybrominated diphenyl ethers (PBDE) PBDE153 Postnatal NA NA Polybrominated diphenyl ether-153 (PBDE-153) in child adjusted for lipids numeric Logarithm base 2 PBDE 153 PBDE153
hs_pbde47_cadj_Log2 hs_pbde47_cadj_Log2 Chemicals Polybrominated diphenyl ethers (PBDE) PBDE47 Postnatal NA NA Polybrominated diphenyl ether-47 (PBDE-47) in child adjusted for lipids numeric Logarithm base 2 PBDE 47 PBDE47
hs_pfhxs_c_Log2 hs_pfhxs_c_Log2 Chemicals Per- and polyfluoroalkyl substances (PFAS) PFHXS Postnatal NA NA Perfluorohexane sulfonate (PFHXS) in child numeric Logarithm base 2 PFHXS PFHXS
hs_pfna_c_Log2 hs_pfna_c_Log2 Chemicals Per- and polyfluoroalkyl substances (PFAS) PFNA Postnatal NA NA Perfluorononanoate (PFNA) in child numeric Logarithm base 2 PFNA PFNA
hs_pfoa_c_Log2 hs_pfoa_c_Log2 Chemicals Per- and polyfluoroalkyl substances (PFAS) PFOA Postnatal NA NA Perfluorooctanoate (PFOA) in child numeric Logarithm base 2 PFOA PFOA
hs_pfos_c_Log2 hs_pfos_c_Log2 Chemicals Per- and polyfluoroalkyl substances (PFAS) PFOS Postnatal NA NA Perfluorooctane sulfonate (PFOS) in child numeric Logarithm base 2 PFOS PFOS
hs_pfunda_c_Log2 hs_pfunda_c_Log2 Chemicals Per- and polyfluoroalkyl substances (PFAS) PFUNDA Postnatal NA NA Perfluoroundecanoate (PFUNDA) in child numeric Logarithm base 2 PFUNDA PFUNDA
hs_bpa_cadj_Log2 hs_bpa_cadj_Log2 Chemicals Phenols BPA Postnatal NA NA Bisphenol A (BPA) in child adjusted for creatinine numeric Logarithm base 2 BPA BPA
hs_bupa_cadj_Log2 hs_bupa_cadj_Log2 Chemicals Phenols BUPA Postnatal NA NA N-Butyl paraben (BUPA) in child adjusted for creatinine numeric Logarithm base 2 BUPA BUPA
hs_etpa_cadj_Log2 hs_etpa_cadj_Log2 Chemicals Phenols ETPA Postnatal NA NA Ethyl paraben (ETPA) in child adjusted for creatinine numeric Logarithm base 2 ETPA ETPA
hs_mepa_cadj_Log2 hs_mepa_cadj_Log2 Chemicals Phenols MEPA Postnatal NA NA Methyl paraben (MEPA) in child adjusted for creatinine numeric Logarithm base 2 MEPA MEPA
hs_oxbe_cadj_Log2 hs_oxbe_cadj_Log2 Chemicals Phenols OXBE Postnatal NA NA Oxybenzone (OXBE) in child adjusted for creatinine numeric Logarithm base 2 OXBE OXBE
hs_prpa_cadj_Log2 hs_prpa_cadj_Log2 Chemicals Phenols PRPA Postnatal NA NA Propyl paraben (PRPA) in child adjusted for creatinine numeric Logarithm base 2 PRPA PRPA
hs_trcs_cadj_Log2 hs_trcs_cadj_Log2 Chemicals Phenols TRCS Postnatal NA NA Triclosan (TRCS) in child adjusted for creatinine numeric Logarithm base 2 TRCS TRCS
hs_mbzp_cadj_Log2 hs_mbzp_cadj_Log2 Chemicals Phthalates MBZP Postnatal NA NA Mono benzyl phthalate (MBzP) in child adjusted for creatinine numeric Logarithm base 2 MBZP MBZP
hs_mecpp_cadj_Log2 hs_mecpp_cadj_Log2 Chemicals Phthalates MECPP Postnatal NA NA Mono-2-ethyl 5-carboxypentyl phthalate (MECPP) in child adjusted for creatinine numeric Logarithm base 2 MECPP MECPP
hs_mehhp_cadj_Log2 hs_mehhp_cadj_Log2 Chemicals Phthalates MEHHP Postnatal NA NA Mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP) in child adjusted for creatinine numeric Logarithm base 2 MEHHP MEHHP
hs_mehp_cadj_Log2 hs_mehp_cadj_Log2 Chemicals Phthalates MEHP Postnatal NA NA Mono-2-ethylhexyl phthalate (MEHP) in child adjusted for creatinine numeric Logarithm base 2 MEHP MEHP
hs_meohp_cadj_Log2 hs_meohp_cadj_Log2 Chemicals Phthalates MEOHP Postnatal NA NA Mono-2-ethyl-5-oxohexyl phthalate (MEOHP) in child adjusted for creatinine numeric Logarithm base 2 MEOHP MEOHP
hs_mep_cadj_Log2 hs_mep_cadj_Log2 Chemicals Phthalates MEP Postnatal NA NA Monoethyl phthalate (MEP) in child adjusted for creatinine numeric Logarithm base 2 MEP MEP
hs_mibp_cadj_Log2 hs_mibp_cadj_Log2 Chemicals Phthalates MIBP Postnatal NA NA Mono-iso-butyl phthalate (MiBP) in child adjusted for creatinine numeric Logarithm base 2 MIBP MIBP
hs_mnbp_cadj_Log2 hs_mnbp_cadj_Log2 Chemicals Phthalates MNBP Postnatal NA NA Mono-n-butyl phthalate (MnBP) in child adjusted for creatinine numeric Logarithm base 2 MNBP MNBP
hs_ohminp_cadj_Log2 hs_ohminp_cadj_Log2 Chemicals Phthalates OHMiNP Postnatal NA NA Mono-4-methyl-7-hydroxyoctyl phthalate (OHMiNP) in child adjusted for creatinine numeric Logarithm base 2 OHMiNP OHMiNP
hs_oxominp_cadj_Log2 hs_oxominp_cadj_Log2 Chemicals Phthalates OXOMINP Postnatal NA NA Mono-4-methyl-7-oxooctyl phthalate (OXOMiNP) in child adjusted for creatinine numeric Logarithm base 2 OXOMINP OXOMINP
hs_sumDEHP_cadj_Log2 hs_sumDEHP_cadj_Log2 Chemicals Phthalates DEHP Postnatal NA NA Sum of DEHP metabolites (µg/g) in child adjusted for creatinine numeric Logarithm base 2 DEHP SumDEHP
FAS_cat_None FAS_cat_None Chemicals Social and economic capital Economic capital Postnatal NA NA Family affluence score factor None Family affluence FamAfl
hs_contactfam_3cat_num_None hs_contactfam_3cat_num_None Chemicals Social and economic capital Social capital Postnatal NA NA scoial capital: family friends factor None Social contact SocCont
hs_hm_pers_None hs_hm_pers_None Chemicals Social and economic capital Social capital Postnatal NA NA How many people live in your home? numeric None House crowding HouseCrow
hs_participation_3cat_None hs_participation_3cat_None Chemicals Social and economic capital Social capital Postnatal NA NA social capital: structural factor None Social participation SocPartic
hs_cotinine_cdich_None hs_cotinine_cdich_None Chemicals Tobacco Smoke Cotinine Postnatal NA NA Dichotomous variable of cotinine in child factor None Cotinine Cotinine
hs_globalexp2_None hs_globalexp2_None Chemicals Tobacco Smoke Tobacco Smoke Postnatal NA NA Global exposure of the child to ETS (2 categories) factor None ETS ETS
hs_smk_parents_None hs_smk_parents_None Chemicals Tobacco Smoke Tobacco Smoke Postnatal NA NA Tobacco Smoke status of parents (both) factor None Smoking_parents SmokPar

Data Summary for Exposures, Covariates, and Outcome

Data Summary Exposures: Lifestyles

Lifestyle_Exposures <- filtered_codebook$variable_name[filtered_codebook$domain=="Lifestyles"]
lifestyle_exposome <- exposome %>%
  select(all_of(Lifestyle_Exposures))
summarytools::view(dfSummary(lifestyle_exposome, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 h_bfdur_Ter [factor]
1. (0,10.8]
2. (10.8,34.9]
3. (34.9,Inf]
506(38.9%)
270(20.8%)
525(40.4%)
0 (0.0%)
2 hs_bakery_prod_Ter [factor]
1. (0,2]
2. (2,6]
3. (6,Inf]
345(26.5%)
423(32.5%)
533(41.0%)
0 (0.0%)
3 hs_beverages_Ter [factor]
1. (0,0.132]
2. (0.132,1]
3. (1,Inf]
331(25.4%)
454(34.9%)
516(39.7%)
0 (0.0%)
4 hs_break_cer_Ter [factor]
1. (0,1.1]
2. (1.1,5.5]
3. (5.5,Inf]
291(22.4%)
521(40.0%)
489(37.6%)
0 (0.0%)
5 hs_caff_drink_Ter [factor]
1. (0,0.132]
2. (0.132,Inf]
808(62.1%)
493(37.9%)
0 (0.0%)
6 hs_dairy_Ter [factor]
1. (0,14.6]
2. (14.6,25.6]
3. (25.6,Inf]
359(27.6%)
465(35.7%)
477(36.7%)
0 (0.0%)
7 hs_fastfood_Ter [factor]
1. (0,0.132]
2. (0.132,0.5]
3. (0.5,Inf]
143(11.0%)
603(46.3%)
555(42.7%)
0 (0.0%)
8 hs_KIDMED_None [numeric]
Mean (sd) : 2.9 (1.8)
min ≤ med ≤ max:
-3 ≤ 3 ≤ 9
IQR (CV) : 2 (0.6)
13 distinct values 0 (0.0%)
9 hs_mvpa_prd_alt_None [numeric]
Mean (sd) : 37.9 (23.1)
min ≤ med ≤ max:
-27.8 ≤ 34.7 ≤ 146.8
IQR (CV) : 24.5 (0.6)
847 distinct values 0 (0.0%)
10 hs_org_food_Ter [factor]
1. (0,0.132]
2. (0.132,1]
3. (1,Inf]
429(33.0%)
396(30.4%)
476(36.6%)
0 (0.0%)
11 hs_proc_meat_Ter [factor]
1. (0,1.5]
2. (1.5,4]
3. (4,Inf]
366(28.1%)
471(36.2%)
464(35.7%)
0 (0.0%)
12 hs_readymade_Ter [factor]
1. (0,0.132]
2. (0.132,0.5]
3. (0.5,Inf]
327(25.1%)
296(22.8%)
678(52.1%)
0 (0.0%)
13 hs_sd_wk_None [numeric]
Mean (sd) : 235.8 (126.7)
min ≤ med ≤ max:
3.1 ≤ 210 ≤ 994.3
IQR (CV) : 127.1 (0.5)
368 distinct values 0 (0.0%)
14 hs_total_bread_Ter [factor]
1. (0,7]
2. (7,17.5]
3. (17.5,Inf]
431(33.1%)
381(29.3%)
489(37.6%)
0 (0.0%)
15 hs_total_cereal_Ter [factor]
1. (0,14.1]
2. (14.1,23.6]
3. (23.6,Inf]
418(32.1%)
442(34.0%)
441(33.9%)
0 (0.0%)
16 hs_total_fish_Ter [factor]
1. (0,1.5]
2. (1.5,3]
3. (3,Inf]
389(29.9%)
454(34.9%)
458(35.2%)
0 (0.0%)
17 hs_total_fruits_Ter [factor]
1. (0,7]
2. (7,14.1]
3. (14.1,Inf]
413(31.7%)
407(31.3%)
481(37.0%)
0 (0.0%)
18 hs_total_lipids_Ter [factor]
1. (0,3]
2. (3,7]
3. (7,Inf]
397(30.5%)
403(31.0%)
501(38.5%)
0 (0.0%)
19 hs_total_meat_Ter [factor]
1. (0,6]
2. (6,9]
3. (9,Inf]
425(32.7%)
411(31.6%)
465(35.7%)
0 (0.0%)
20 hs_total_potatoes_Ter [factor]
1. (0,3]
2. (3,4]
3. (4,Inf]
417(32.1%)
405(31.1%)
479(36.8%)
0 (0.0%)
21 hs_total_sweets_Ter [factor]
1. (0,4.1]
2. (4.1,8.5]
3. (8.5,Inf]
344(26.4%)
516(39.7%)
441(33.9%)
0 (0.0%)
22 hs_total_veg_Ter [factor]
1. (0,6]
2. (6,8.5]
3. (8.5,Inf]
404(31.1%)
314(24.1%)
583(44.8%)
0 (0.0%)
23 hs_total_yog_Ter [factor]
1. (0,6]
2. (6,8.5]
3. (8.5,Inf]
779(59.9%)
308(23.7%)
214(16.4%)
0 (0.0%)
24 hs_dif_hours_total_None [numeric]
Mean (sd) : 10.3 (0.7)
min ≤ med ≤ max:
7.9 ≤ 10.3 ≤ 12.9
IQR (CV) : 0.9 (0.1)
437 distinct values 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

#separate numeric and categorical data
numeric_lifestyle <- lifestyle_exposome %>% 
  select(where(is.numeric))

numeric_lifestyle_long <- pivot_longer(
  numeric_lifestyle,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_numerical_vars <- unique(numeric_lifestyle_long$variable)

num_plots <- lapply(unique_numerical_vars, function(var) {
  data <- filter(numeric_lifestyle_long, variable == var)
  p <- ggplot(data, aes(x = value)) +
    geom_histogram(bins = 30, fill = "blue") +
    labs(title = paste("Histogram of", var), x = "Value", y = "Count")
  print(p)
  return(p)
})

categorical_lifestyle <- lifestyle_exposome %>% 
  select(where(is.factor))

categorical_lifestyle_long <- pivot_longer(
  categorical_lifestyle,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_categorical_vars <- unique(categorical_lifestyle_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
  data <- filter(categorical_lifestyle_long, variable == var)
  
  p <- ggplot(data, aes(x = value, fill = value)) +
    geom_bar(stat = "count") +
    labs(title = paste("Distribution of", var), x = var, y = "Count")
  
  print(p)
  return(p)
})

numeric_lifestyle <- select_if(lifestyle_exposome, is.numeric)
cor_matrix <- cor(numeric_lifestyle, method = "pearson")
cor_matrix <- cor(numeric_lifestyle, method = "spearman")
corrplot(cor_matrix, method = "circle")

Data Summary Exposures: Chemicals

Chemical_Exposures <- filtered_codebook$variable_name[filtered_codebook$domain=="Chemicals"]
chemical_exposome <- exposome %>%
  select(all_of(Chemical_Exposures))
summarytools::view(dfSummary(chemical_exposome, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 hs_as_c_Log2 [numeric]
Mean (sd) : -1 (3.3)
min ≤ med ≤ max:
-15 ≤ 0.5 ≤ 4.8
IQR (CV) : 5.3 (-3.3)
692 distinct values 0 (0.0%)
2 hs_cd_c_Log2 [numeric]
Mean (sd) : -4 (1)
min ≤ med ≤ max:
-10.4 ≤ -3.8 ≤ 0.8
IQR (CV) : 1 (-0.3)
695 distinct values 0 (0.0%)
3 hs_co_c_Log2 [numeric]
Mean (sd) : -2.3 (0.6)
min ≤ med ≤ max:
-5.5 ≤ -2.4 ≤ 1.4
IQR (CV) : 0.7 (-0.3)
317 distinct values 0 (0.0%)
4 hs_cs_c_Log2 [numeric]
Mean (sd) : 0.4 (0.6)
min ≤ med ≤ max:
-1.5 ≤ 0.5 ≤ 3.1
IQR (CV) : 0.8 (1.3)
369 distinct values 0 (0.0%)
5 hs_cu_c_Log2 [numeric]
Mean (sd) : 9.8 (0.2)
min ≤ med ≤ max:
9.1 ≤ 9.8 ≤ 12.1
IQR (CV) : 0.3 (0)
345 distinct values 0 (0.0%)
6 hs_hg_c_Log2 [numeric]
Mean (sd) : -0.3 (1.7)
min ≤ med ≤ max:
-10.9 ≤ -0.2 ≤ 3.7
IQR (CV) : 2.1 (-5.6)
698 distinct values 0 (0.0%)
7 hs_mn_c_Log2 [numeric]
Mean (sd) : 3.1 (0.4)
min ≤ med ≤ max:
1.7 ≤ 3.1 ≤ 4.8
IQR (CV) : 0.6 (0.1)
457 distinct values 0 (0.0%)
8 hs_mo_c_Log2 [numeric]
Mean (sd) : -0.3 (0.9)
min ≤ med ≤ max:
-9.2 ≤ -0.4 ≤ 5.1
IQR (CV) : 0.8 (-2.9)
593 distinct values 0 (0.0%)
9 hs_pb_c_Log2 [numeric]
Mean (sd) : 3.1 (0.6)
min ≤ med ≤ max:
1.1 ≤ 3.1 ≤ 7.7
IQR (CV) : 0.8 (0.2)
529 distinct values 0 (0.0%)
10 hs_tl_cdich_None [factor]
1. Detected
2. Undetected
102(7.8%)
1199(92.2%)
0 (0.0%)
11 hs_dde_cadj_Log2 [numeric]
Mean (sd) : 4.7 (1.5)
min ≤ med ≤ max:
1.2 ≤ 4.5 ≤ 11.1
IQR (CV) : 1.9 (0.3)
1050 distinct values 0 (0.0%)
12 hs_ddt_cadj_Log2 [numeric]
Mean (sd) : -1.6 (3.7)
min ≤ med ≤ max:
-15.4 ≤ -0.5 ≤ 7.6
IQR (CV) : 2.5 (-2.3)
1039 distinct values 0 (0.0%)
13 hs_hcb_cadj_Log2 [numeric]
Mean (sd) : 3.2 (0.9)
min ≤ med ≤ max:
-13.1 ≤ 3.1 ≤ 6.5
IQR (CV) : 0.9 (0.3)
1036 distinct values 0 (0.0%)
14 hs_pcb118_cadj_Log2 [numeric]
Mean (sd) : 1.1 (0.8)
min ≤ med ≤ max:
-7 ≤ 1 ≤ 4.8
IQR (CV) : 1 (0.7)
1048 distinct values 0 (0.0%)
15 hs_pcb138_cadj_Log2 [numeric]
Mean (sd) : 2.4 (1.1)
min ≤ med ≤ max:
-9.4 ≤ 2.4 ≤ 7.7
IQR (CV) : 1.4 (0.5)
1031 distinct values 0 (0.0%)
16 hs_pcb153_cadj_Log2 [numeric]
Mean (sd) : 3.6 (0.9)
min ≤ med ≤ max:
1.2 ≤ 3.5 ≤ 7.8
IQR (CV) : 1.4 (0.3)
1047 distinct values 0 (0.0%)
17 hs_pcb170_cadj_Log2 [numeric]
Mean (sd) : -0.3 (3)
min ≤ med ≤ max:
-16.8 ≤ 0.3 ≤ 4.8
IQR (CV) : 2.2 (-9.8)
1039 distinct values 0 (0.0%)
18 hs_pcb180_cadj_Log2 [numeric]
Mean (sd) : 1.7 (1.9)
min ≤ med ≤ max:
-11.7 ≤ 1.8 ≤ 5.9
IQR (CV) : 2.3 (1.1)
1055 distinct values 0 (0.0%)
19 hs_sumPCBs5_cadj_Log2 [numeric]
Mean (sd) : 4.6 (1)
min ≤ med ≤ max:
2.2 ≤ 4.6 ≤ 9.3
IQR (CV) : 1.5 (0.2)
1052 distinct values 0 (0.0%)
20 hs_dep_cadj_Log2 [numeric]
Mean (sd) : 0.2 (3.2)
min ≤ med ≤ max:
-12.6 ≤ 0.9 ≤ 9.4
IQR (CV) : 3.3 (20)
1045 distinct values 0 (0.0%)
21 hs_detp_cadj_Log2 [numeric]
Mean (sd) : -2.4 (3.6)
min ≤ med ≤ max:
-15.4 ≤ -3.3 ≤ 6.3
IQR (CV) : 6 (-1.5)
1036 distinct values 0 (0.0%)
22 hs_dmdtp_cdich_None [factor]
1. Detected
2. Undetected
227(17.4%)
1074(82.6%)
0 (0.0%)
23 hs_dmp_cadj_Log2 [numeric]
Mean (sd) : -1.4 (4)
min ≤ med ≤ max:
-16.6 ≤ -0.3 ≤ 6.4
IQR (CV) : 7 (-2.9)
1053 distinct values 0 (0.0%)
24 hs_dmtp_cadj_Log2 [numeric]
Mean (sd) : 1.1 (2.6)
min ≤ med ≤ max:
-10.6 ≤ 1.6 ≤ 8.7
IQR (CV) : 2.4 (2.3)
1057 distinct values 0 (0.0%)
25 hs_pbde153_cadj_Log2 [numeric]
Mean (sd) : -4.5 (3.8)
min ≤ med ≤ max:
-17.6 ≤ -2.6 ≤ 4
IQR (CV) : 6.7 (-0.8)
1036 distinct values 0 (0.0%)
26 hs_pbde47_cadj_Log2 [numeric]
Mean (sd) : -2.6 (2.5)
min ≤ med ≤ max:
-15.4 ≤ -2.1 ≤ 5.4
IQR (CV) : 1.2 (-1)
1010 distinct values 0 (0.0%)
27 hs_pfhxs_c_Log2 [numeric]
Mean (sd) : -1.6 (1.3)
min ≤ med ≤ max:
-8.9 ≤ -1.4 ≤ 4.8
IQR (CV) : 1.7 (-0.8)
1061 distinct values 0 (0.0%)
28 hs_pfna_c_Log2 [numeric]
Mean (sd) : -1.1 (1.1)
min ≤ med ≤ max:
-8.1 ≤ -1.1 ≤ 2.7
IQR (CV) : 1.3 (-1)
1031 distinct values 0 (0.0%)
29 hs_pfoa_c_Log2 [numeric]
Mean (sd) : 0.6 (0.6)
min ≤ med ≤ max:
-2.2 ≤ 0.6 ≤ 2.7
IQR (CV) : 0.7 (0.9)
1061 distinct values 0 (0.0%)
30 hs_pfos_c_Log2 [numeric]
Mean (sd) : 1 (1.1)
min ≤ med ≤ max:
-10.4 ≤ 1 ≤ 5.1
IQR (CV) : 1.3 (1.1)
1050 distinct values 0 (0.0%)
31 hs_pfunda_c_Log2 [numeric]
Mean (sd) : -4.2 (1.6)
min ≤ med ≤ max:
-11.8 ≤ -4.1 ≤ 0.6
IQR (CV) : 1.7 (-0.4)
1044 distinct values 0 (0.0%)
32 hs_bpa_cadj_Log2 [numeric]
Mean (sd) : 2.1 (1.5)
min ≤ med ≤ max:
-7.2 ≤ 2 ≤ 7.8
IQR (CV) : 1.6 (0.7)
1056 distinct values 0 (0.0%)
33 hs_bupa_cadj_Log2 [numeric]
Mean (sd) : -3.5 (2)
min ≤ med ≤ max:
-13.9 ≤ -3.5 ≤ 6.6
IQR (CV) : 1.8 (-0.6)
1034 distinct values 0 (0.0%)
34 hs_etpa_cadj_Log2 [numeric]
Mean (sd) : -0.1 (1.9)
min ≤ med ≤ max:
-6.1 ≤ -0.6 ≤ 11
IQR (CV) : 1.6 (-14.3)
1066 distinct values 0 (0.0%)
35 hs_mepa_cadj_Log2 [numeric]
Mean (sd) : 3.4 (2.5)
min ≤ med ≤ max:
-6.9 ≤ 2.7 ≤ 14.5
IQR (CV) : 3 (0.7)
1052 distinct values 0 (0.0%)
36 hs_oxbe_cadj_Log2 [numeric]
Mean (sd) : 1.5 (2.4)
min ≤ med ≤ max:
-4.1 ≤ 1.1 ≤ 13
IQR (CV) : 3 (1.6)
1069 distinct values 0 (0.0%)
37 hs_prpa_cadj_Log2 [numeric]
Mean (sd) : -1.6 (3.8)
min ≤ med ≤ max:
-12 ≤ -2.3 ≤ 10.8
IQR (CV) : 5.2 (-2.4)
1031 distinct values 0 (0.0%)
38 hs_trcs_cadj_Log2 [numeric]
Mean (sd) : -0.4 (2)
min ≤ med ≤ max:
-4.4 ≤ -0.7 ≤ 9.3
IQR (CV) : 2.2 (-5.6)
1053 distinct values 0 (0.0%)
39 hs_mbzp_cadj_Log2 [numeric]
Mean (sd) : 2.4 (1.2)
min ≤ med ≤ max:
-0.6 ≤ 2.3 ≤ 7.2
IQR (CV) : 1.5 (0.5)
1046 distinct values 0 (0.0%)
40 hs_mecpp_cadj_Log2 [numeric]
Mean (sd) : 5.2 (1.1)
min ≤ med ≤ max:
2.6 ≤ 5.1 ≤ 10.6
IQR (CV) : 1.5 (0.2)
1037 distinct values 0 (0.0%)
41 hs_mehhp_cadj_Log2 [numeric]
Mean (sd) : 4.4 (1.1)
min ≤ med ≤ max:
1.8 ≤ 4.4 ≤ 11.1
IQR (CV) : 1.4 (0.2)
1050 distinct values 0 (0.0%)
42 hs_mehp_cadj_Log2 [numeric]
Mean (sd) : 1.6 (1.2)
min ≤ med ≤ max:
-1.6 ≤ 1.6 ≤ 8.1
IQR (CV) : 1.5 (0.7)
1035 distinct values 0 (0.0%)
43 hs_meohp_cadj_Log2 [numeric]
Mean (sd) : 3.7 (1.1)
min ≤ med ≤ max:
1.1 ≤ 3.6 ≤ 10.3
IQR (CV) : 1.5 (0.3)
1057 distinct values 0 (0.0%)
44 hs_mep_cadj_Log2 [numeric]
Mean (sd) : 5.3 (1.6)
min ≤ med ≤ max:
1.7 ≤ 5.1 ≤ 11.6
IQR (CV) : 2.2 (0.3)
1075 distinct values 0 (0.0%)
45 hs_mibp_cadj_Log2 [numeric]
Mean (sd) : 5.5 (1.1)
min ≤ med ≤ max:
2.3 ≤ 5.4 ≤ 9.8
IQR (CV) : 1.5 (0.2)
1057 distinct values 0 (0.0%)
46 hs_mnbp_cadj_Log2 [numeric]
Mean (sd) : 4.7 (1)
min ≤ med ≤ max:
1.9 ≤ 4.6 ≤ 8.9
IQR (CV) : 1.3 (0.2)
1048 distinct values 0 (0.0%)
47 hs_ohminp_cadj_Log2 [numeric]
Mean (sd) : 2.6 (1.2)
min ≤ med ≤ max:
-0.3 ≤ 2.4 ≤ 9.1
IQR (CV) : 1.5 (0.5)
1085 distinct values 0 (0.0%)
48 hs_oxominp_cadj_Log2 [numeric]
Mean (sd) : 1.7 (1.2)
min ≤ med ≤ max:
-0.9 ≤ 1.5 ≤ 9.4
IQR (CV) : 1.4 (0.7)
1059 distinct values 0 (0.0%)
49 hs_sumDEHP_cadj_Log2 [numeric]
Mean (sd) : 6 (1.2)
min ≤ med ≤ max:
2.6 ≤ 6 ≤ 10.1
IQR (CV) : 1.6 (0.2)
1028 distinct values 0 (0.0%)
50 FAS_cat_None [factor]
1. Low
2. Middle
3. High
146(11.2%)
486(37.4%)
669(51.4%)
0 (0.0%)
51 hs_contactfam_3cat_num_None [factor]
1. (almost) Daily
2. Once a week
3. Less than once a week
863(66.3%)
382(29.4%)
56(4.3%)
0 (0.0%)
52 hs_hm_pers_None [numeric]
Mean (sd) : 4.2 (1)
min ≤ med ≤ max:
1 ≤ 4 ≤ 10
IQR (CV) : 1 (0.2)
1:2(0.2%)
2:36(2.8%)
3:180(13.8%)
4:670(51.5%)
5:297(22.8%)
6:85(6.5%)
7:17(1.3%)
8:8(0.6%)
9:5(0.4%)
10:1(0.1%)
0 (0.0%)
53 hs_participation_3cat_None [factor]
1. None
2. 1 organisation
3. 2 or more organisations
748(57.5%)
355(27.3%)
198(15.2%)
0 (0.0%)
54 hs_cotinine_cdich_None [factor]
1. Detected
2. Undetected
223(17.1%)
1078(82.9%)
0 (0.0%)
55 hs_globalexp2_None [factor]
1. exposure
2. no exposure
463(35.6%)
838(64.4%)
0 (0.0%)
56 hs_smk_parents_None [factor]
1. both
2. neither
3. one
142(10.9%)
814(62.6%)
345(26.5%)
0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

#separate numeric and categorical data
numeric_chemical <- chemical_exposome %>% 
  select(where(is.numeric))

numeric_chemical_long <- pivot_longer(
  numeric_chemical,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_numerical_vars <- unique(numeric_chemical_long$variable)

num_plots <- lapply(unique_numerical_vars, function(var) {
  data <- filter(numeric_chemical_long, variable == var)
  p <- ggplot(data, aes(x = value)) +
    geom_histogram(bins = 30, fill = "blue") +
    labs(title = paste("Histogram of", var), x = "Value", y = "Count")
  print(p)
  return(p)
})

categorical_chemical <- chemical_exposome %>% 
  select(where(is.factor))

categorical_chemical_long <- pivot_longer(
  categorical_lifestyle,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_categorical_vars <- unique(categorical_chemical_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
  data <- filter(categorical_chemical_long, variable == var)
  
  p <- ggplot(data, aes(x = value, fill = value)) +
    geom_bar(stat = "count") +
    labs(title = paste("Distribution of", var), x = var, y = "Count")
  
  print(p)
  return(p)
})

numeric_chemical <- select_if(chemical_exposome, is.numeric)
cor_matrix <- cor(numeric_chemical, method = "pearson")
cor_matrix <- cor(numeric_chemical, method = "spearman")
custom_color_scale <- list(
  c(0, "darkred"),    
  c(0.5, "white"), 
  c(1, "darkblue")
)

plot_ly(
  z = cor_matrix, 
  x = colnames(cor_matrix), 
  y = colnames(cor_matrix), 
  type = "heatmap",
  colorscale = custom_color_scale
) %>%
layout(
  title = "Correlation Matrix",
  xaxis = list(tickangle = -90),
  yaxis = list(side = "left")
)

Data Summary Covariates

summarytools::view(dfSummary(covariates, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ID [integer]
Mean (sd) : 651 (375.7)
min ≤ med ≤ max:
1 ≤ 651 ≤ 1301
IQR (CV) : 650 (0.6)
1301 distinct values (Integer sequence) 0 (0.0%)
2 h_cohort [factor]
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
202(15.5%)
198(15.2%)
224(17.2%)
207(15.9%)
272(20.9%)
198(15.2%)
0 (0.0%)
3 e3_sex_None [factor]
1. female
2. male
608(46.7%)
693(53.3%)
0 (0.0%)
4 e3_yearbir_None [factor]
1. 2003
2. 2004
3. 2005
4. 2006
5. 2007
6. 2008
7. 2009
55(4.2%)
107(8.2%)
241(18.5%)
256(19.7%)
250(19.2%)
379(29.1%)
13(1.0%)
0 (0.0%)
5 h_mbmi_None [numeric]
Mean (sd) : 25 (5.2)
min ≤ med ≤ max:
15.9 ≤ 24 ≤ 51.4
IQR (CV) : 6.1 (0.2)
853 distinct values 0 (0.0%)
6 hs_wgtgain_None [numeric]
Mean (sd) : 13.5 (6.2)
min ≤ med ≤ max:
0 ≤ 12 ≤ 55
IQR (CV) : 9 (0.5)
49 distinct values 0 (0.0%)
7 e3_gac_None [numeric]
Mean (sd) : 39.6 (1.7)
min ≤ med ≤ max:
28 ≤ 40 ≤ 44.1
IQR (CV) : 2 (0)
72 distinct values 0 (0.0%)
8 h_age_None [numeric]
Mean (sd) : 30.8 (4.9)
min ≤ med ≤ max:
16 ≤ 31 ≤ 43.5
IQR (CV) : 6.4 (0.2)
665 distinct values 0 (0.0%)
9 h_edumc_None [factor]
1. 1
2. 2
3. 3
178(13.7%)
449(34.5%)
674(51.8%)
0 (0.0%)
10 h_native_None [factor]
1. 0
2. 1
3. 2
146(11.2%)
67(5.1%)
1088(83.6%)
0 (0.0%)
11 h_parity_None [factor]
1. 0
2. 1
3. 2
601(46.2%)
464(35.7%)
236(18.1%)
0 (0.0%)
12 hs_child_age_None [numeric]
Mean (sd) : 8 (1.6)
min ≤ med ≤ max:
5.4 ≤ 8 ≤ 12.1
IQR (CV) : 2.4 (0.2)
879 distinct values 0 (0.0%)
13 hs_c_height_None [numeric]
Mean (sd) : 1.3 (0.1)
min ≤ med ≤ max:
1.1 ≤ 1.3 ≤ 1.7
IQR (CV) : 0.2 (0.1)
311 distinct values 0 (0.0%)
14 hs_c_weight_None [numeric]
Mean (sd) : 28.5 (7.7)
min ≤ med ≤ max:
16 ≤ 26.9 ≤ 71.1
IQR (CV) : 9.8 (0.3)
311 distinct values 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

#separate numeric and categorical data
numeric_covariates <- covariates %>% 
  select(where(is.numeric))

numeric_covariates_long <- pivot_longer(
  numeric_covariates,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_numerical_vars <- unique(numeric_covariates_long$variable)

num_plots <- lapply(unique_numerical_vars, function(var) {
  data <- filter(numeric_covariates_long, variable == var)
  p <- ggplot(data, aes(x = value)) +
    geom_histogram(bins = 30, fill = "blue") +
    labs(title = paste("Histogram of", var), x = "Value", y = "Count")
  print(p)
  return(p)
})

categorical_covariates <- covariates %>% 
  select(where(is.factor))

categorical_covariates_long <- pivot_longer(
  categorical_covariates,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_categorical_vars <- unique(categorical_covariates_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
  data <- filter(categorical_covariates_long, variable == var)
  
  p <- ggplot(data, aes(x = value, fill = value)) +
    geom_bar(stat = "count") +
    labs(title = paste("Distribution of", var), x = var, y = "Count")
  
  print(p)
  return(p)
})

numeric_covariate <- select_if(covariates, is.numeric)
cor_matrix <- cor(numeric_covariate, method = "pearson")
cor_matrix <- cor(numeric_covariate, method = "spearman")
corrplot(cor_matrix, method = "circle")

Data Summary Outcome: Phenotype

outcome_BMI <- phenotype %>% 
  select(hs_zbmi_who)
summarytools::view(dfSummary(outcome_BMI, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 hs_zbmi_who [numeric]
Mean (sd) : 0.4 (1.2)
min ≤ med ≤ max:
-3.6 ≤ 0.3 ≤ 4.7
IQR (CV) : 1.5 (3)
421 distinct values 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

Metabolomic Serum Data

First 10 rows and columns of the metabolomic serum data

load(paste0(work.dir, "/metabol_serum.RData"))
kable(metabol_serum.d[1:10,1:10], align="c", digits=2, format="pipe")
430 1187 940 936 788 698 380 196 114 885
metab_1 -2.15 -0.69 -0.69 -0.19 -1.96 -1.90 -0.22 -1.38 -0.54 -1.25
metab_2 -0.71 -0.37 -0.36 -0.34 -0.35 -0.63 -0.26 -0.46 -0.44 -0.48
metab_3 8.60 9.15 8.95 8.54 8.73 8.24 9.03 8.29 8.37 8.18
metab_4 0.55 -1.33 -0.13 -0.62 -0.80 -0.46 0.49 0.12 -0.76 -0.07
metab_5 7.05 6.89 7.10 7.01 6.90 6.94 6.77 6.62 6.85 7.24
metab_6 5.79 5.81 5.86 5.95 5.95 5.42 5.82 5.65 5.44 5.60
metab_7 3.75 4.26 4.35 4.24 4.88 4.70 4.08 4.73 3.98 4.30
metab_8 5.07 5.08 5.92 5.41 5.39 4.62 5.10 5.28 4.51 5.45
metab_9 -1.87 -2.30 -1.97 -1.89 -1.55 -1.78 -2.29 -1.64 -2.02 -1.68
metab_10 -2.77 -3.42 -3.40 -2.84 -2.45 -3.14 -3.36 -2.88 -3.05 -2.92